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Autor(en): 
  • Russell B. Millar
  • Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2011  
    Genre:  Schulbücher 
     
    Angewandte Wahrscheinlichkeitsrechnung u. Statistik / Applied Probability & Statistics / Biometrics / Biometrie / Biostatistics / Biostatistik / Maximum-Likelihood-Methode / Statistics / Statistik
    ISBN:  9780470094822 
    EAN-Code: 
    9780470094822 
    Verlag:  Wiley 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Statistics in Practice  
    Dimensionen:  H 229 mm / B 152 mm / D 24 mm 
    Gewicht:  668 gr 
    Seiten:  376 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: * Provides an accessible introduction to pragmatic maximum likelihood modelling. * Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. * Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. * Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. * Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. * Provides all program code and software extensions on a supporting website. * Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.

      



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